def get_omniglot_loaders(arguments):
if arguments.preload_all_data: raise NotImplementedError
train_loader = torch.utils.data.DataLoader(datasets.Omniglot(
DATASET_PATH,
background=True,
download=True,
transform=transforms.Compose([
transforms.RandomAffine(10),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=arguments.batch_size,
shuffle=True,
pin_memory=True,
num_workers=NUM_WORKERS)
test_loader = torch.utils.data.DataLoader(
datasets.Omniglot(
DATASET_PATH,
background=False,
download=True,
transform=transforms.Compose([ # transforms.RandomCrop(70),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=arguments.batch_size,
shuffle=True,
pin_memory=True,
num_workers=NUM_WORKERS)
return train_loader, test_loader
def get_inverted_omniglot_loaders(arguments, mean=(0.5,), std=(0.5,)):
print("Using mean", mean)
# (1-0.92206,), (0.08426,)
if arguments['preload_all_data']: raise NotImplementedError
train_loader = torch.utils.data.DataLoader(
datasets.Omniglot(DATASET_PATH, background=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: 1 - x),
transforms.Normalize((1 - mean), (1 - std))
])),
batch_size=arguments['batch_size'],
shuffle=True,
pin_memory=True,
num_workers=NUM_WORKERS
)
test_loader = torch.utils.data.DataLoader(
datasets.Omniglot(DATASET_PATH, background=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: 1 - x),
transforms.Normalize((1 - mean), (1 - std))
])),
batch_size=arguments['batch_size'],
shuffle=False,
pin_memory=True,
num_workers=NUM_WORKERS
)
return train_loader, test_loader
def main():
controller = Controller()
kwargs = {"num_workers": 1, "pin_memory": True} if FLAGS.CUDA else {}
train_loader = DataLoader(
datasets.Omniglot(
"data",
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(mean, std) should add for better performance, + other transforms
])),
batch_sizer=FLAGS.BATCH_SIZE,
shuffle=True,
**kwargs)
test_loader = DataLoader(
datasets.Omniglot(
"data",
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(mean, std) should add for better performance, + other transforms
])),
batch_sizer=FLAGS.BATCH_SIZE,
shuffle=True,
**kwargs)
if FLAGS.TRAIN:
train(controller, train_loader)
else:
test(controller, test_loader)
def _get_dataset(self):
print("Loading {} dataset from {}.".format(self.dataset_name, self.datadir))
augment_transforms = []
image_transforms = tfs.Compose([tfs.ToTensor()])
if self.dataset_name == 'omniglot':
self.input_shape, self.num_classes = (1, 105, 105), 1623
self.train_dataset = datasets.Omniglot(self.datadir,
background=True,
target_transform=None,
download=True,
transform=image_transforms)
self.test_dataset = datasets.Omniglot(self.datadir,
background=False,
target_transform=None,
download=True,
transform=image_transforms)
elif self.dataset_name == 'cifar100':
self.input_shape, self.num_classes = (3, 32, 32), 100
if self.augment:
print("Using augmentation on train dataset.")
augment_transforms = [tfs.RandomCrop(32, padding=4),
tfs.RandomHorizontalFlip()]
image_transforms = [tfs.ToTensor(),
tfs.Normalize(mean=[0.507, 0.487, 0.441],
std=[0.267, 0.256, 0.276])]
train_transforms = tfs.Compose(augment_transforms + image_transforms)
test_transforms = tfs.Compose(image_transforms)
self.train_dataset = datasets.CIFAR100(self.datadir,
train=True,
download=True,
transform=train_transforms)
self.test_dataset = datasets.CIFAR100(self.datadir,
train=False,
download=True,
transform=test_transforms)
elif self.dataset_name == 'cifar10':
self.input_shape, self.num_classes = (3, 32, 32), 10
self.train_dataset = datasets.CIFAR10(self.datadir,
train=True,
download=True,
transform=image_transforms)
self.test_dataset = datasets.CIFAR10(self.datadir,
train=False,
download=True,
transform=image_transforms)
else:
raise Exception("{} dataset not found!".format(self.dataset_name))
def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_workers=1):
self.data_dir = data_dir
dataset = datasets.Omniglot(self.data_dir, download=True, background=True)
eval_dataset = datasets.Omniglot(self.data_dir, download=True, background=False)
target_transform = OmniglotTargetTransform(self.data_dir, background=True)
eval_target_transform = OmniglotTargetTransform(self.data_dir, background=False)
self.dataset = datasets.Omniglot(self.data_dir, background=True, download=True, transform=omni_transforms, target_transform=target_transform)
self.eval_dataset = datasets.Omniglot(self.data_dir, background=False, download=True, transform=omni_transforms, target_transform=eval_target_transform)
self.targets = np.array([self.dataset[i][1] for i in range(len(self.dataset))])
eval_targets = np.array([self.eval_dataset[i][1] for i in range(len(self.eval_dataset))])
super().__init__(self.dataset, batch_size, shuffle, validation_split,
num_workers, self.targets, drop_train_last=False,
drop_valid_last=False, evaluation=(self.eval_dataset, eval_targets))
def omniglot():
return itertools.chain(*[
collect_download_configs(
lambda: datasets.Omniglot(
ROOT, background=background, download=True),
name=f"Omniglot, {'background' if background else 'evaluation'}",
) for background in (True, False)
])
def download(self):
origin_dir = 'data/omniglot-py'
processed_dir = self.root_dir
dset.Omniglot(root='data', background=False, download=True)
dset.Omniglot(root='data', background=True, download=True)
try:
os.mkdir(processed_dir)
except OSError:
pass
for p in ['images_background', 'images_evaluation']:
for f in os.listdir(os.path.join(origin_dir, p)):
shutil.move(os.path.join(origin_dir, p, f), processed_dir)
shutil.rmtree(origin_dir)
def __init__(self, height=32, length=32):
self.channels = 1
self.height = height
self.length = length
self.data = datasets.Omniglot(root='./data', download=True)
self.make_tasks()
self.split_validation_and_training_task()
self.resize = transforms.Resize((self.height, self.length))
self.to_tensor = transforms.ToTensor()
def __init__(self, height=32, length=32):
self.channels = 1
self.height = height
self.length = length
self.data = datasets.Omniglot(root='./data', download=True)
self.task_maker()
self.split_dataset()
self.resize = transforms.Resize((self.height, self.length))
self.tensor = transforms.ToTensor()
def __init__(self, height=32, length=32):
self.channels = 1
self.height = height
self.length = length
self.data = datasets.Omniglot(
root=
'C:/Users/kashi/Documents/CMPE_258/proj/FIGR-master/FIGR-master/data/',
download=True)
self.make_tasks()
self.split_validation_and_training_task()
self.resize = transforms.Resize((self.height, self.length))
self.to_tensor = transforms.ToTensor()
def load_data_and_initialize_loaders(data_name, train_batch, test_batch):
data_name = data_name.lower()
kwargs = {'num_workers': 1, 'pin_memory': True}
if data_name == 'mnist':
train_data = datasets.MNIST('./data',
train=True,
download=True,
transform=transforms.ToTensor())
test_data = datasets.MNIST('./data',
train=False,
transform=transforms.ToTensor())
elif data_name == 'fashion' or data_name == 'fashionmnist':
train_data = datasets.FashionMNIST('./data',
train=True,
download=True,
transform=transforms.ToTensor())
test_data = datasets.FashionMNIST('./data',
train=False,
transform=transforms.ToTensor())
elif data_name == 'omniglot':
train_data = datasets.Omniglot(root='./data',
background=True,
download=True,
transform=transforms.ToTensor())
test_data = datasets.Omniglot(root='./data',
background=False,
download=True,
transform=transforms.ToTensor())
# else: raise Exception("Data name not recognized")
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=train_batch,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=test_batch,
shuffle=True,
**kwargs)
return train_loader, test_loader
def load_omniglot(args):
torch.cuda.manual_seed(1)
kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True}
path = 'data_o/'
if args.scratch:
path = '/scratch/eecs-share/ratzlafn/' + path
train_loader = torch.utils.data.DataLoader(datasets.Omniglot(
path,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=32,
shuffle=True,
**kwargs)
return train_loader
def few_shot_omniglot(root, train=True, batch_size=128, timesteps=15,
n_jobs=0, resize=28, **kwargs):
train_set = dset.Omniglot(root=root, download=True, background=train,
transform=transforms.Compose([
transforms.Resize([resize, resize], interpolation=Image.NEAREST),
transforms.ToTensor(),
transforms.Lambda(lambda x: 1 - x)
]))
collate = FewShotCollate(timesteps=timesteps)
sampler = FewShotSampler(
num_cls=len(train_set._character_images),
batch_size=batch_size, separate=True,
timesteps=timesteps, **kwargs,
img_per_class=len(train_set._character_images[0]),
)
return torch.utils.data.DataLoader(
dataset=train_set, batch_sampler=sampler,
collate_fn=collate, num_workers=n_jobs
)
def __load_data(subset) -> pd.DataFrame:
# 必要があればデータをダウンロード
datasets.Omniglot(root=config.DATA_PATH,
download=True,
background=(subset == 'background'))
# プログレスバー表示のため，全量をカウント
print(f'loading omniglot dataset ({subset})')
total_images = 0
for root, folders, files in os.walk(
f'{config.DATA_PATH}/omniglot-py/images_{subset}/'):
total_images += len(files)
# ファイルシステムを参照し，画像データに属性を付与してDataFrameをつくる
progress = tqdm(total=total_images)
images = list()
for root, folders, files in os.walk(
f'{config.DATA_PATH}/omniglot-py/images_{subset}/'):
alphabet = root.split('/')[-2]
class_name = alphabet + '.' + root.split('/')[-1]
for f in files:
images.append({
'subset': subset,
'alphabet': alphabet,
'class_name': class_name,
'filepath': os.path.join(root, f)
})
progress.update(1)
progress.close()
# DataFrameに変換
df = pd.DataFrame(images)
df = df.assign(id=df.index.values) # indexに応じた値をIDカラムとして追加
unique_characters = sorted(df['class_name'].unique())
num_classes = len(df['class_name'].unique())
class_name_to_id = {
unique_characters[i]: i
for i in range(num_classes)
}
df = df.assign(
class_id=df['class_name'].apply(lambda c: class_name_to_id[
c])) # クラスごとにユニークなIDを振り，class_nameカラムとして追加
return df
def load_omniglot(root_dir=None,
batch_size=20,
shuffle=True,
transform=None,
download=True):
dataset_type = "binary"
if root_dir is None:
root_dir = pathlib.Path(sys.argv[0]).parents[0] / 'datasets'
# root_dir = str(root_dir)
if transform is None:
transform = transforms.ToTensor()
train_dataset = datasets.Omniglot(root_dir,
transform=transform,
download=download)
test_dataset = datasets.Omniglot(root_dir,
transform=transform,
download=download,
background=False)
train_data = np.zeros((len(train_dataset), 105, 105))
test_data = np.zeros((len(test_dataset), 105, 105))
for i, (image, _) in enumerate(train_dataset):
train_data[i] = image.numpy() / 255
for i, (image, _) in enumerate(test_dataset):
test_data[i] = image.numpy() / 255
if shuffle:
np.random.shuffle(train_data)
np.random.shuffle(test_data)
train_data = torch.from_numpy(train_data)
test_data = torch.from_numpy(test_data)
# no labels
train_labels = torch.zeros(train_data.shape)
test_labels = torch.zeros(test_data.shape)
train_dataset = data_utils.TensorDataset(train_data.float(), train_labels)
test_dataset = data_utils.TensorDataset(test_data.float(), test_labels)
size_train = len(train_dataset)
indices = list(range(size_train))
val_split = size_train - 1345 # given by god to make the batch size reasonable
train_idx, valid_idx = indices[:val_split], indices[val_split:]
train_sampler = SubsetSampler(train_idx)
valid_sampler = SubsetSampler(valid_idx)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
sampler=train_sampler,
shuffle=False,
)
valid_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
sampler=valid_sampler,
shuffle=False)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=shuffle)
return train_loader, test_loader, valid_loader, dataset_type
def main():
# Training settings
parser = argparse.ArgumentParser(description='Amortized approximation on MNIST')
parser.add_argument('--batch-size', type=int, default=256, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=64, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--approx-epochs', type=int, default=200, metavar='N',
help='number of epochs to approx (default: 10)')
parser.add_argument('--lr', type=float, default=1e-2, metavar='LR',
help='learning rate (default: 0.0005)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--S', type=int, default=100, metavar='N',
help='number of posterior samples from the Bayesian model')
parser.add_argument('--model-path', type=str, default='../saved_models/mnist_sgld/', metavar='N',
help='number of posterior samples from the Bayesian model')
parser.add_argument('--from-approx-model', type=int, default=1, metavar='N',
help='if our model is loaded or trained')
parser.add_argument('--test-ood-from-disk', type=int, default=1,
help='generate test samples or load from disk')
parser.add_argument('--ood-name', type=str, default='omniglot',
help='name of the used ood dataset')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {}
tr_data = MNIST('../data', train=True, transform=transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))]), download=True)
te_data = MNIST('../data', train=False, transform=transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))]), download=True)
train_loader = torch.utils.data.DataLoader(
tr_data,
batch_size=args.batch_size, shuffle=False, **kwargs)
test_loader = torch.utils.data.DataLoader(
te_data,
batch_size=args.batch_size, shuffle=False, **kwargs)
if args.ood_name == 'omniglot':
ood_data = datasets.Omniglot('../../data', download=True, transform=transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
]))
elif args.ood_name == 'SEMEION':
ood_data = datasets.SEMEION('../../data', download=True, transform=transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
]))
ood_loader = torch.utils.data.DataLoader(
ood_data,
batch_size=args.batch_size, shuffle=False, **kwargs)
model = mnist_mlp(dropout=False).to(device)
model.load_state_dict(torch.load(args.model_path + 'sgld-mnist.pt'))
test(args, model, device, test_loader)
if args.from_approx_model == 0:
output_samples = torch.load(args.model_path + 'mnist-sgld-train-samples.pt')
# --------------- training approx ---------
fmodel = mnist_mlp_h().to(device)
gmodel = mnist_mlp_g().to(device)
if args.from_approx_model == 0:
g_optimizer = optim.SGD(gmodel.parameters(), lr=args.lr)
f_optimizer = optim.SGD(fmodel.parameters(), lr=args.lr)
best_acc = 0
for epoch in range(1, args.approx_epochs + 1):
train_approx(args, fmodel, gmodel, device, train_loader, f_optimizer, g_optimizer, output_samples, epoch)
acc = test(args, fmodel, device, test_loader)
# if (args.save_approx_model == 1):
if acc > best_acc:
torch.save(fmodel.state_dict(), args.model_path + 'sgld-mnist-mmd-mean.pt')
torch.save(gmodel.state_dict(), args.model_path + 'sgld-mnist-mmd-conc.pt')
best_acc = acc
else:
fmodel.load_state_dict(torch.load(args.model_path + 'sgld-mnist-mmd-mean.pt'))
gmodel.load_state_dict(torch.load(args.model_path + 'sgld-mnist-mmd-conc.pt'))
print('generating teacher particles for testing&ood data ...')
# generate particles for test and ood dataset
model.train()
if args.test_ood_from_disk == 1:
teacher_test_samples = torch.load(args.model_path + 'mnist-sgld-test-samples.pt')
else:
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
for data, target in test_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1,0,2)
torch.save(all_samples, args.model_path + 'mnist-sgld-test-samples.pt')
teacher_test_samples = all_samples
if args.test_ood_from_disk == 1:
teacher_ood_samples = torch.load(args.model_path + 'mnist-sgld-' + args.ood_name + '-samples.pt')
else:
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
for data, target in ood_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1,0,2)
torch.save(all_samples, args.model_path + 'mnist-sgld-' + args.ood_name + '-samples.pt')
teacher_ood_samples = all_samples
eval_approx(args, fmodel, gmodel, device, test_loader, ood_loader, teacher_test_samples, teacher_ood_samples)
def run(seed):
assert torch.cuda.is_available()
device = torch.device('cuda')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
np.random.seed(seed)
torch.manual_seed(seed)
# Create training data.
data_transform = tvtransforms.Compose(
[tvtransforms.ToTensor(),
tvtransforms.Lambda(torch.bernoulli)])
if args.dataset_name == 'mnist':
dataset = datasets.MNIST(root=os.path.join(utils.get_data_root(),
'mnist'),
train=True,
download=True,
transform=data_transform)
test_dataset = datasets.MNIST(root=os.path.join(
utils.get_data_root(), 'mnist'),
train=False,
download=True,
transform=data_transform)
elif args.dataset_name == 'fashion-mnist':
dataset = datasets.FashionMNIST(root=os.path.join(
utils.get_data_root(), 'fashion-mnist'),
train=True,
download=True,
transform=data_transform)
test_dataset = datasets.FashionMNIST(root=os.path.join(
utils.get_data_root(), 'fashion-mnist'),
train=False,
download=True,
transform=data_transform)
elif args.dataset_name == 'omniglot':
dataset = datasets.Omniglot(root=os.path.join(utils.get_data_root(),
'omniglot'),
train=False,
download=True,
transform=data_transform)
test_dataset = datasets.Omniglot(root=os.path.join(
utils.get_data_root(), 'omniglot'),
train=False,
download=True,
transform=data_transform)
elif args.dataset_name == 'emnist':
rotate = partial(tvF.rotate, angle=-90)
hflip = tvF.hflip
data_transform = tvtransforms.Compose([
tvtransforms.Lambda(rotate),
tvtransforms.Lambda(hflip),
tvtransforms.ToTensor(),
tvtransforms.Lambda(torch.bernoulli)
])
dataset = datasets.EMNIST(root=os.path.join(utils.get_data_root(),
'emnist'),
split='letters',
train=True,
transform=data_transform,
download=True)
test_dataset = datasets.EMNIST(root=os.path.join(
utils.get_data_root(), 'emnist'),
split='letters',
train=False,
transform=data_transform,
download=True)
else:
raise ValueError
if args.dataset_name == 'omniglot':
split = -1345
elif args.dataset_name == 'emnist':
split = -20000
else:
split = -10000
indices = np.arange(len(dataset))
np.random.shuffle(indices)
train_indices, val_indices = indices[:split], indices[split:]
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)
train_loader = data.DataLoader(
dataset=dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=4 if args.dataset_name == 'emnist' else 0)
train_generator = data_.batch_generator(train_loader)
val_loader = data.DataLoader(dataset=dataset,
batch_size=1024,
sampler=val_sampler,
shuffle=False,
drop_last=False)
val_batch = next(iter(val_loader))[0]
test_loader = data.DataLoader(
test_dataset,
batch_size=16,
shuffle=False,
drop_last=False,
)
def create_linear_transform():
if args.linear_type == 'lu':
return transforms.CompositeTransform([
transforms.RandomPermutation(args.latent_features),
transforms.LULinear(args.latent_features, identity_init=True)
])
elif args.linear_type == 'svd':
return transforms.SVDLinear(args.latent_features,
num_householder=4,
identity_init=True)
elif args.linear_type == 'perm':
return transforms.RandomPermutation(args.latent_features)
else:
raise ValueError
def create_base_transform(i, context_features=None):
if args.prior_type == 'affine-coupling':
return transforms.AffineCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=args.latent_features, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm))
elif args.prior_type == 'rq-coupling':
return transforms.PiecewiseRationalQuadraticCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=args.latent_features, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm),
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
apply_unconditional_transform=args.
apply_unconditional_transform,
)
elif args.prior_type == 'rl-coupling':
return transforms.PiecewiseRationalLinearCouplingTransform(
mask=utils.create_alternating_binary_mask(
features=args.latent_features, even=(i % 2 == 0)),
transform_net_create_fn=lambda in_features, out_features: nn_.
ResidualNet(in_features=in_features,
out_features=out_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm),
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
apply_unconditional_transform=args.
apply_unconditional_transform,
)
elif args.prior_type == 'affine-autoregressive':
return transforms.MaskedAffineAutoregressiveTransform(
features=args.latent_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_blocks=args.num_transform_blocks,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm)
elif args.prior_type == 'rq-autoregressive':
return transforms.MaskedPiecewiseRationalQuadraticAutoregressiveTransform(
features=args.latent_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
num_blocks=args.num_transform_blocks,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm)
elif args.prior_type == 'rl-autoregressive':
return transforms.MaskedPiecewiseRationalLinearAutoregressiveTransform(
features=args.latent_features,
hidden_features=args.hidden_features,
context_features=context_features,
num_bins=args.num_bins,
tails='linear',
tail_bound=args.tail_bound,
num_blocks=args.num_transform_blocks,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=args.dropout_probability,
use_batch_norm=args.use_batch_norm)
else:
raise ValueError
# ---------------
# prior
# ---------------
def create_prior():
if args.prior_type == 'standard-normal':
prior = distributions_.StandardNormal((args.latent_features, ))
else:
distribution = distributions_.StandardNormal(
(args.latent_features, ))
transform = transforms.CompositeTransform([
transforms.CompositeTransform(
[create_linear_transform(),
create_base_transform(i)])
for i in range(args.num_flow_steps)
])
transform = transforms.CompositeTransform(
[transform, create_linear_transform()])
prior = flows.Flow(transform, distribution)
return prior
# ---------------
# inputs encoder
# ---------------
def create_inputs_encoder():
if args.approximate_posterior_type == 'diagonal-normal':
inputs_encoder = None
else:
inputs_encoder = nn_.ConvEncoder(
context_features=args.context_features,
channels_multiplier=16,
dropout_probability=args.dropout_probability_encoder_decoder)
return inputs_encoder
# ---------------
# approximate posterior
# ---------------
def create_approximate_posterior():
if args.approximate_posterior_type == 'diagonal-normal':
context_encoder = nn_.ConvEncoder(
context_features=args.context_features,
channels_multiplier=16,
dropout_probability=args.dropout_probability_encoder_decoder)
approximate_posterior = distributions_.ConditionalDiagonalNormal(
shape=[args.latent_features], context_encoder=context_encoder)
else:
context_encoder = nn.Linear(args.context_features,
2 * args.latent_features)
distribution = distributions_.ConditionalDiagonalNormal(
shape=[args.latent_features], context_encoder=context_encoder)
transform = transforms.CompositeTransform([
transforms.CompositeTransform([
create_linear_transform(),
create_base_transform(
i, context_features=args.context_features)
]) for i in range(args.num_flow_steps)
])
transform = transforms.CompositeTransform(
[transform, create_linear_transform()])
approximate_posterior = flows.Flow(
transforms.InverseTransform(transform), distribution)
return approximate_posterior
# ---------------
# likelihood
# ---------------
def create_likelihood():
latent_decoder = nn_.ConvDecoder(
latent_features=args.latent_features,
channels_multiplier=16,
dropout_probability=args.dropout_probability_encoder_decoder)
likelihood = distributions_.ConditionalIndependentBernoulli(
shape=[1, 28, 28], context_encoder=latent_decoder)
return likelihood
prior = create_prior()
approximate_posterior = create_approximate_posterior()
likelihood = create_likelihood()
inputs_encoder = create_inputs_encoder()
model = vae.VariationalAutoencoder(
prior=prior,
approximate_posterior=approximate_posterior,
likelihood=likelihood,
inputs_encoder=inputs_encoder)
n_params = utils.get_num_parameters(model)
print('There are {} trainable parameters in this model.'.format(n_params))
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer=optimizer, T_max=args.num_training_steps, eta_min=0)
def get_kl_multiplier(step):
if args.kl_multiplier_schedule == 'constant':
return args.kl_multiplier_initial
elif args.kl_multiplier_schedule == 'linear':
multiplier = min(
step / (args.num_training_steps * args.kl_warmup_fraction), 1.)
return args.kl_multiplier_initial * (1. + multiplier)
# create summary writer and write to log directory
timestamp = cutils.get_timestamp()
if cutils.on_cluster():
timestamp += '||{}'.format(os.environ['SLURM_JOB_ID'])
log_dir = os.path.join(cutils.get_log_root(), args.dataset_name, timestamp)
while True:
try:
writer = SummaryWriter(log_dir=log_dir, max_queue=20)
break
except FileExistsError:
sleep(5)
filename = os.path.join(log_dir, 'config.json')
with open(filename, 'w') as file:
json.dump(vars(args), file)
best_val_elbo = -np.inf
tbar = tqdm(range(args.num_training_steps))
for step in tbar:
model.train()
optimizer.zero_grad()
batch = next(train_generator)[0].to(device)
elbo = model.stochastic_elbo(batch,
kl_multiplier=get_kl_multiplier(step))
loss = -torch.mean(elbo)
loss.backward()
optimizer.step()
scheduler.step(step)
if (step + 1) % args.monitor_interval == 0:
model.eval()
with torch.no_grad():
elbo = model.stochastic_elbo(val_batch.to(device))
mean_val_elbo = elbo.mean()
if mean_val_elbo > best_val_elbo:
best_val_elbo = mean_val_elbo
path = os.path.join(
cutils.get_checkpoint_root(),
'{}-best-val-{}.t'.format(args.dataset_name, timestamp))
torch.save(model.state_dict(), path)
writer.add_scalar(tag='val-elbo',
scalar_value=mean_val_elbo,
global_step=step)
writer.add_scalar(tag='best-val-elbo',
scalar_value=best_val_elbo,
global_step=step)
with torch.no_grad():
samples = model.sample(64)
fig, ax = plt.subplots(figsize=(10, 10))
cutils.gridimshow(make_grid(samples.view(64, 1, 28, 28), nrow=8),
ax)
writer.add_figure(tag='vae-samples', figure=fig, global_step=step)
plt.close()
# load best val model
path = os.path.join(
cutils.get_checkpoint_root(),
'{}-best-val-{}.t'.format(args.dataset_name, timestamp))
model.load_state_dict(torch.load(path))
model.eval()
np.random.seed(5)
torch.manual_seed(5)
# compute elbo on test set
with torch.no_grad():
elbo = torch.Tensor([])
log_prob_lower_bound = torch.Tensor([])
for batch in tqdm(test_loader):
elbo_ = model.stochastic_elbo(batch[0].to(device))
elbo = torch.cat([elbo, elbo_])
log_prob_lower_bound_ = model.log_prob_lower_bound(
batch[0].to(device), num_samples=1000)
log_prob_lower_bound = torch.cat(
[log_prob_lower_bound, log_prob_lower_bound_])
path = os.path.join(
log_dir, '{}-prior-{}-posterior-{}-elbo.npy'.format(
args.dataset_name, args.prior_type,
args.approximate_posterior_type))
np.save(path, utils.tensor2numpy(elbo))
path = os.path.join(
log_dir, '{}-prior-{}-posterior-{}-log-prob-lower-bound.npy'.format(
args.dataset_name, args.prior_type,
args.approximate_posterior_type))
np.save(path, utils.tensor2numpy(log_prob_lower_bound))
# save elbo and log prob lower bound
mean_elbo = elbo.mean()
std_elbo = elbo.std()
mean_log_prob_lower_bound = log_prob_lower_bound.mean()
std_log_prob_lower_bound = log_prob_lower_bound.std()
s = 'ELBO: {:.2f} +- {:.2f}, LOG PROB LOWER BOUND: {:.2f} +- {:.2f}'.format(
mean_elbo.item(), 2 * std_elbo.item() / np.sqrt(len(test_dataset)),
mean_log_prob_lower_bound.item(),
2 * std_log_prob_lower_bound.item() / np.sqrt(len(test_dataset)))
filename = os.path.join(log_dir, 'test-results.txt')
with open(filename, 'w') as file:
file.write(s)
def _get_dataset(self, train: bool, transform: Any) -> torch.utils.data.Dataset:
return datasets.Omniglot(self.data_folder, background=train, download=False, transform=transform)
import torch
import torch.nn.functional as F
import utils
from torchvision import datasets, transforms
import VAE_model as vae
import easy_conv_vae as conv_vae
import maml_class
import numpy as np
import pickle
from torch import optim
import matplotlib.pyplot as plt
import random
from tqdm import tqdm
import seaborn as sns
data = datasets.Omniglot(root='./data', download=True)
source_task_number=500
task_set_number = 200
data_set = utils.get_dataset(data,50, 20, 10)
random.shuffle(data_set)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
epochs=200
betas = [1.]
for j,beta in enumerate(betas):
model = conv_vae.VAE(z_dim=128)
model.to(device)
maml = maml_class.MAML(model=model, data=data_set, inner_lr=1e-2,
def train_gan(opt):
os.makedirs(os.path.join(opt.savingroot, opt.dataset, 'images'),
exist_ok=True)
os.makedirs(os.path.join(opt.savingroot, opt.dataset, 'chkpts'),
exist_ok=True)
#Build networ
if opt.model_type == 'sa':
AC = False
if opt.loss_type == 'Projection':
AC = False
elif opt.loss_type == 'Twin_AC':
AC = True
elif opt.loss_type == 'AC':
AC = True
netd_g = nn.DataParallel(
SA_Discriminator(n_class=opt.num_classes,
nc=opt.nc,
AC=AC,
Resolution=opt.image_size,
ch=64).cuda())
netg = nn.DataParallel(
SA_Generator(n_class=opt.num_classes,
code_dim=opt.nz,
nc=opt.nc,
SN=opt.SN,
Resolution=opt.image_size,
ch=32).cuda())
elif opt.model_type == 'big':
AC = False
if opt.loss_type == 'Projection':
AC = False
elif opt.loss_type == 'Twin_AC':
AC = True
elif opt.loss_type == 'AC':
AC = True
netd_g = nn.DataParallel(
Discriminator(n_classes=opt.num_classes,
resolution=opt.image_size,
AC=AC).cuda())
netg = nn.DataParallel(
Generator(n_classes=opt.num_classes,
resolution=opt.image_size,
SN=opt.SN).cuda())
if opt.data_r == 'MNIST':
dataset = dset.MNIST(root=opt.dataroot, download=True, transform=tsfm)
elif opt.data_r == 'CIFAR10':
dataset = dset.CIFAR10(root=opt.dataroot,
download=True,
transform=tsfm)
elif opt.data_r == 'CIFAR100':
dataset = dset.CIFAR100(root=opt.dataroot,
download=True,
transform=tsfm)
elif opt.data_r == 'CUB':
dataset = dset.ImageFolder(
root='/home/yanwuxu/CUB_200_2011_processed/ImageNet/ImageNet/',
transform=tsfm)
elif opt.data_r == 'VGGFACE':
dataset = ILSVRC_HDF5(root='../data/VGGFACE64.hdf5', transform=tsfm)
elif opt.data_r == 'IMAGENET100':
dataset = Load_numpy_data(root='../data/ImageNet100.pt',
transform=tsfm)
elif opt.data_r == 'MNIST_overlap':
dataset = Load_gray_data(root='../data/overlap_MNIST.pt',
transform=tsfm)
elif opt.data_r == 'OMNIGLOT':
dataset = dset.Omniglot(
'../result', transform=tsfm,
download=True) #Load_gray_data(root='omniglot.pt', transform=tsfm)
print('training_start')
print(opt.loss_type)
step = 0
train_g(netd_g, netg, dataset, step, opt)
import utils
import easy_conv_vae as conv_vae
import pickle
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
number_of_tasks = 500
train_number = 20 * number_of_tasks
transform = transforms.Compose(
[transforms.Resize((28, 28)),
transforms.ToTensor()])
data = datasets.Omniglot(root='./data', transform=transform)
train_set = list(data)[:train_number]
test_set = list(data)[train_number:train_number + 1000]
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=20,
shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=20,
shuffle=True)
model = conv_vae.VAE(z_dim=128).to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
epochs = 20
beta = 1.
# Reconstruction + KL divergence losses summed over all elements and batch
nn.init.normal_(m.weight,0,2e-2)
nn.init.normal_(m.bias, 0.5, 1e-2)
#testing
net=Siamese_Net()
# print(net)
dummyx1=torch.randn(64,1,105,105)
dummyx2=torch.randn(64,1,105,105)
o=net(dummyx1,dummyx2)
print(o.shape)
"""# **Omniglot Dataset**"""
# download the dataset
omni_train=datasets.Omniglot(root='/content',
background= True,
download = True
)
omni_test=datasets.Omniglot(root='/content',
background= False,
download = True
)
# Omniglot dataset- didnt rename cause would have had to rename it everywhere
class Face_Dataset(Dataset):
def __init__(self,root_dir, job='train',ways=10,transform=None):
super(Face_Dataset,self).__init__()
self.root_dir=root_dir
self.job=job
self.all_classes=os.listdir(root_dir)
self.num_classes=len(self.all_classes)
def main():
# Training settings
parser = argparse.ArgumentParser(
description='run approximation to LeNet on Mnist')
parser.add_argument('--batch-size',
type=int,
default=256,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=100,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=10,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.001,
metavar='LR',
help='learning rate (default: 0.0005)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--dropout-rate',
type=float,
default=0.5,
metavar='p_drop',
help='dropout rate')
parser.add_argument(
'--S',
type=int,
default=500,
metavar='N',
help='number of posterior samples from the Bayesian model')
parser.add_argument(
'--model-path',
type=str,
default='../saved_models/mnist_sgld/',
metavar='N',
help='number of posterior samples from the Bayesian model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])),
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
model = mnist_mlp(dropout=False).to(device)
optimizer = SGLD(model.parameters(), lr=args.lr)
import copy
import pickle as pkl
for epoch in range(1, args.epochs + 1):
train_bayesian(args, model, device, train_loader, optimizer, epoch)
print("epoch: {}".format(epoch))
test(args, model, device, test_loader)
# save models
torch.save(model.state_dict(), args.model_path + 'sgld-mnist.pt')
# save samples
param_samples = []
while (1):
for idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
data = data.view(data.shape[0], -1)
output = model(data)
loss = F.nll_loss(F.log_softmax(output, dim=1), target)
loss.backward()
optimizer.step()
param_samples.append(copy.deepcopy(model.state_dict()))
if param_samples.__len__() >= args.S:
print('1', len(param_samples))
break
if param_samples.__len__() >= args.S:
print('2', len(param_samples))
break
with open(args.model_path + "sgld_samples.pkl", "wb") as f:
print('3', len(param_samples))
pkl.dump(param_samples, f)
test(args, model, device, test_loader)
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])),
batch_size=args.batch_size,
shuffle=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])),
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
# generate teacher train samples
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
model.load_state_dict(param_samples[i])
for data, target in train_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1, 0, 2)
torch.save(all_samples,
args.model_path + 'mnist-sgld-train-samples.pt')
# generate teacher test samples
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
model.load_state_dict(param_samples[i])
for data, target in test_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1, 0, 2)
torch.save(all_samples, args.model_path + 'mnist-sgld-test-samples.pt')
# generate teacher omniglot samples
ood_data = datasets.Omniglot(
'../../data',
download=True,
transform=transforms.Compose([
# transforms.ToPILImage(),
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, )),
]))
ood_loader = torch.utils.data.DataLoader(ood_data,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
model.load_state_dict(param_samples[i])
for data, target in ood_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1, 0, 2)
torch.save(all_samples,
args.model_path + 'mnist-sgld-omniglot-samples.pt')
# generate teacher SEMEION samples
ood_data = datasets.SEMEION(
'../../data',
download=True,
transform=transforms.Compose([
# transforms.ToPILImage(),
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, )),
]))
ood_loader = torch.utils.data.DataLoader(ood_data,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
with torch.no_grad():
# obtain ensemble outputs
all_samples = []
for i in range(500):
samples_a_round = []
model.load_state_dict(param_samples[i])
for data, target in ood_loader:
data = data.to(device)
data = data.view(data.shape[0], -1)
output = F.softmax(model(data))
samples_a_round.append(output)
samples_a_round = torch.cat(samples_a_round).cpu()
all_samples.append(samples_a_round)
all_samples = torch.stack(all_samples).permute(1, 0, 2)
torch.save(all_samples,
args.model_path + 'mnist-sgld-SEMEION-samples.pt')
def get_datasets(
dataset_name,
frac_val=FRAC_VAL,
batch_size=8,
img_shape=None,
nn_architecture=None,
train_params=None,
synthetic_params=None,
class_2d=None,
kwargs=None,
):
if kwargs is None:
kwargs = KWARGS
img_shape_no_channel = None
if img_shape is not None:
img_shape_no_channel = img_shape[1:]
# TODO(nina): Consistency in datasets: add channels for all
logging.info("Loading data from dataset: %s" % dataset_name)
if dataset_name == "mnist":
train_dataset, val_dataset = get_dataset_mnist()
elif dataset_name == "omniglot":
if img_shape_no_channel is not None:
transform = transforms.Compose([
transforms.Resize(img_shape_no_channel),
transforms.ToTensor()
])
else:
transform = transforms.ToTensor()
dataset = datasets.Omniglot("../data",
download=True,
transform=transform)
train_dataset, val_dataset = split_dataset(dataset, frac_val=frac_val)
elif dataset_name in [
"cryo_sim",
"randomrot1D_nodisorder",
"randomrot1D_multiPDB",
"randomrot_nodisorder",
]:
dataset = get_dataset_cryo(dataset_name, img_shape_no_channel, kwargs)
train_dataset, val_dataset = split_dataset(dataset)
elif dataset_name == "cryo_sphere":
dataset = get_dataset_cryo_sphere(img_shape_no_channel, kwargs)
train_dataset, val_dataset = split_dataset(dataset)
elif dataset_name == "cryo_exp":
dataset = get_dataset_cryo_exp(img_shape_no_channel, kwargs)
train_dataset, val_dataset = split_dataset(dataset)
elif dataset_name == "cryo_exp_class_2d":
dataset = get_dataset_cryo_exp_class_2d(img_shape_no_channel,
class_2d) # , kwargs)
train_dataset, val_dataset = split_dataset(dataset)
elif dataset_name == "cryo_exp_3d":
dataset = get_dataset_cryo_exp_3d(img_shape_no_channel, kwargs)
train_dataset, val_dataset = split_dataset(dataset)
elif dataset_name == "connectomes":
train_dataset, val_dataset = get_dataset_connectomes(
img_shape_no_channel=img_shape_no_channel)
elif dataset_name == "connectomes_simu":
train_dataset, val_dataset = get_dataset_connectomes_simu(
img_shape_no_channel=img_shape_no_channel)
elif dataset_name == "connectomes_schizophrenia":
train_dataset, val_dataset, _ = get_dataset_connectomes_schizophrenia()
elif dataset_name in ["mri", "segmentation", "fmri"]:
train_loader, val_loader = get_loaders_brain(dataset_name, frac_val,
batch_size,
img_shape_no_channel,
kwargs)
return train_loader, val_loader
elif dataset_name == "synthetic":
dataset = make_synthetic_dataset_and_decoder(
synthetic_params=synthetic_params,
nn_architecture=nn_architecture,
train_params=train_params,
)
train_dataset, val_dataset = split_dataset(dataset)
else:
raise ValueError("Unknown dataset name: %s" % dataset_name)
return train_dataset, val_dataset
batch_size = 32
max_length = 15
rescaling = lambda x: (x - .5) * 2.
rescaling_inv = lambda x: .5 * x + .5
flip = lambda x: -x
kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True}
resizing = lambda x: x.resize((28, 28))
omni_transforms = transforms.Compose(
[resizing, transforms.ToTensor(), rescaling,
flip]) #TODO: check this, but i think i don't want rescaling
kwargs = {'num_workers': 1, 'pin_memory': True, 'drop_last': True}
train_loader = torch.utils.data.DataLoader(datasets.Omniglot(
'../vhe/data',
download=True,
background=True,
transform=omni_transforms),
batch_size=batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.Omniglot(
'../vhe/data',
download=True,
background=False,
transform=omni_transforms),
batch_size=batch_size,
shuffle=True,
**kwargs)
def load_dataset(
root,
name,
dataset_size=None,
transform=None,
split="train",
fraction=1.0,
batch_size=None,
):
name = name.lower().replace("-", "_")
assert split in ("train", "test")
if name == "mnist":
data = datasets.MNIST(
os.path.join(root, "MNIST"),
train=split == "train",
download=True,
transform=transform,
)
elif name == "fashion-mnist":
data = datasets.FashionMNIST(
os.path.join(root, "FASHION-MNIST"),
train=split == "train",
download=True,
transform=transform,
)
elif name == "cifar10":
data = datasets.CIFAR10(
os.path.join(root, "CIFAR10"),
train=split == "train",
download=True,
transform=transform,
)
elif name == "svhn":
data = datasets.SVHN(
os.path.join(root, "SVHN"), split=split, download=True, transform=transform
)
elif name == "stl10":
if split == "train":
split += "+unlabeled"
data = datasets.STL10(
os.path.join(root, "STL10"), split=split, download=True, transform=transform
)
elif name == "lsun-bed":
data = datasets.LSUN(
os.path.join(root, "LSUN"), classes=["bedroom_train"], transform=transform
)
elif name == "omniglot":
data = datasets.Omniglot(
os.path.join(root, "OMNIGLOT"),
background=split == "train",
download=True,
transform=transform,
)
elif name.startswith("test"):
_, c, d = name.split("_")
data = datasets.FakeData(
size=dataset_size,
image_size=(int(c), int(d), int(d)),
num_classes=2,
transform=transform,
)
data.labels = np.random.randint(0, 2, dataset_size)
elif name == "scaly":
data = SingleFolderDataset(os.path.join(root, "SCALY"), transform=transform)
elif name == "celeba":
center_crop = transforms.CenterCrop(178)
# deal with non squared celebA
transform = transforms.Compose([center_crop, transform])
data = SingleFolderDataset(
os.path.join(root, "celebA", "img_align_celeba"), transform=transform
)
else:
raise NotImplementedError(name)
assert 0 < fraction <= 1
assert dataset_size is None or dataset_size <= len(data)
if dataset_size is None:
dataset_size = len(data)
size = min(int(fraction * len(data)), dataset_size)
size = max(
size, batch_size if batch_size is not None else 0, torch.cuda.device_count(), 1
)
if size < len(data):
points = np.random.choice(range(len(data)), replace=False, size=size)
data = torch.utils.data.Subset(data, points)
return IgnoreLabelDataset(data)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.view(x.size(0), -1)
return self.logits(x)
if __name__ == "__main__":
trans = transforms.Compose(
[transforms.Resize((28, 28)),
transforms.ToTensor()])
tasks = Omniglot_Task_Distribution(
datasets.Omniglot('./Omniglot/', transform=trans), 20)
N, K = 5, 5
task = tasks.sample_task(N, K, 15)
meta_model = Classifier(N)
maml = MAML(meta_model.cuda(),
tasks,
inner_lr=0.01,
meta_lr=0.001,
K=10,
inner_steps=1,
tasks_per_meta_batch=32,
criterion=nn.CrossEntropyLoss())
maml.main_loop(num_iterations=100)
elif 'cifar' in args.dataset :
train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(args.data_dir, train=True,
download=True, transform=ds_transforms), batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(datasets.CIFAR10(args.data_dir, train=False,
transform=ds_transforms), batch_size=args.batch_size, shuffle=True, **kwargs)
if args.nr_logistic_mix:
loss_op = lambda real, fake : discretized_mix_logistic_loss(real, fake)
sample_op = lambda x : sample_from_discretized_mix_logistic(x, args.nr_logistic_mix)
else:
raise NotImplementedError("No 3D Softmax")
elif 'omni' in args.dataset :
train_loader = torch.utils.data.DataLoader(datasets.Omniglot(args.data_dir, download=True,
background=True, transform=omni_transforms), batch_size=1,
shuffle=True, **kwargs)
#d = datasets.Omniglot(args.data_dir, download=True,
# background=True, transform=omni_transforms)
test_loader = torch.utils.data.DataLoader(datasets.Omniglot(args.data_dir, download=True,
background=False, transform=omni_transforms), batch_size=1,
shuffle=True, **kwargs)
if args.nr_logistic_mix:
loss_op = lambda real, fake : discretized_mix_logistic_loss_1d(real, fake)
sample_op = lambda x : sample_from_discretized_mix_logistic_1d(x, args.nr_logistic_mix)
else:
loss_op = lambda real, fake : softmax_loss_1d(real, fake)
sample_op = lambda x : sample_from_softmax_1d(x)
import torchvision.datasets as tvd
if __name__ == "__main__":
data_dir = "./data"
for train in [True, False]:
tvd.MNIST(data_dir, download=True, train=train)
tvd.CIFAR10(data_dir, download=True, train=train)
tvd.Omniglot(data_dir, download=True, background=train)
def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_workers=1, background=True):
self.data_dir = data_dir
self.dataset = datasets.Omniglot(self.data_dir, background=background, download=True, transform=omni_transforms)
super().__init__(self.dataset, batch_size, shuffle, validation_split, num_workers)
